thackway_may_presentation

CSIRO ES Seminar Series 19 May 2011

Canberra

Managing native vegetation - insights from longitudinal site management histories

Richard ThackwayACEAS Sabbatical Fellow

http://aceas.org.au/index.php?option=com_content&view=article&id=70&Itemid=74

http://aceas.org.au/index.php?option=com_content&view=article&id=70&Itemid=74

www.abare-brs.gov.au

Outline

• TERN and ACEAS

• Definitions and concepts

• The method – vegetation transformations

• A case study

• How might this information be used?

• Where to from here


TERN facilities interaction


Australia’s future landscapes – The big issues and questions

Biodiversity conservation, biodiverse carbon, biosequestration, food security - agriculture moving to northern Australia etc

1. What has happened in this landscape over time e.g. <200yrs?

2. How might historic/ contemporary impacts of land use (LU) and land management practices (LMP) affect future land use options/ decisions?


A model of change in ecosystems

Source: Adamson and Fox (1982). Time

Ch

ang

e i

n v

eg

eta

tio

n v

ari

able

Settlement

10000

Reference


Occupation

Relaxation

Anthropogenic change

Net impact

Time

1800 1850 1900 1950 2000

Mod

ifica

tion

scor

e

Reference

Based on Hamilton, Brown & Nolan (2008). FWPA PRO7.1050. pg 18Land use impacts on biodiversity and Life Cycle Assessment

Transformation pathway


• Public & private NRM agencies

? reporting on the status of resource/s

? developing policy & design programs

? informing priorities for investment in NRM

? monitoring and reporting and improvement following investment

? Developing scenarios and planning

• Researchers

• Education

• Wider community

Drivers for this information?


Land use and management – the primary agents of landscape transformation

• Management of native veg leads to modification, fragmentation, removal and replacement or enhancement

• For example 2010 Australia’s landscapes:

– 9 used for cropping

– 58 used for grazing sheep and cattle

– 0.2 plantations

– 12.8 in conservation reserves

• Numerous studies have identified pressure metrics or indicators of the impacts of LU and LMP

– Result in changes in vegetation structure, composition & function


Solutions to date – snap shots

• Site-based assessments

? Scoring survey sites relative to benchmark sites e.g. BioCondition, Habitat Hectares etc

? State and transition models

• Whole of landscape assessments

? Classifying mapping units relative to reference unmodified /least modified statee.g. VAST (Vegetation Assets States and Transitions) and Vegmachine

9


The problem

Vegetation transformation

∆ VC score

∆ time×

∆ VC score

∆ space/extent

VC = Benchmarked vegetation condition


Why a project of transforming of Australia’s vegetated landscapes?

At the national level

• No approach for compiling sequential land use and management histories

• No consistent approach for assessing the response of vegetation communities to impacts/pressures over time and space

? Regenerative capacity

? Vegetation structure

? Species composition

• No infrastructure to compile a repository of where, when Australia’s vegetated landscapes were and are being transformed


Project aims

• Build on the ‘transitions’ component of the VAST framework

• Develop and test a method for describing the transforming of Australia’s native vegetation by:

? Documenting longitudinal site histories of LU) and LMP

? Developing a system for scoring the responses of native vegetation communities to sequential changes in land use LU and LMP

? Presenting interim results as transformation graphs

• Contribute to developing guidelines for assessing and monitoring the transformation of vegetated landscapes


Literature review and case studies

• Review identified 22 indicators (pressure metrics, anthropogenic disturbances)

• Literature as a resource for case studies

? More anecdotal stories than reliable observations /measurements

? More two date than multi-temporal changes

? More observations of coarse scale than fine scale changes

? More binary/ single comparison of attributes than changes in multi-attribute states (e.g. regen capacity, structure and species)

? More remote sensing than ecological plot-based observations

? More contemporary local than long term landscape change


Data synthesis and hierarchy

Site



Site

Indicators 22



Site

Attribute groups 10

Indicators 22



Site

Diagnostic attributes 3

Attribute groups 10

Indicators 22



Site

Transformation score/site /year 1

Diagnostic attributes 3

Attribute groups 10

Indicators 22


1

3

10

22

Dia

gn

ost

icat

trib

ute

sA

ttri

bu

teg

rou

ps

SpeciesComposition

(2)

UnderstoreyOverstorey

(2)

Indicators

Scoring sites for each year


1

3

10

22

Dia

gn

ost

icat

trib

ute

sA

ttri

bu

teg

rou

ps

VegetationStructure

Overstorey

(3)

Understorey

(3)

SpeciesComposition

(2)


(2)

Indicators



1

3

10

22

Dia

gn

ost

icat

trib

ute

sA

ttri

bu

teg

rou

ps

VegetationStructure

Overstorey

(3)

Understorey

(3)

SpeciesComposition

(2)


(2)

RegenerativeCapacity

Fire

(2)

Reprodpotent

(2)

Soil

Hydrology

(2)

Biology

(2)

Chemistry

(2)

Structure

(2) Indicators



1

3

10

22

Dia

gn

ost

icat

trib

ute

s

VegetationTransformation

score

Att

rib

ute

gro

up

s

VegetationStructure

Overstorey

(3)

Understorey

(3)

SpeciesComposition

(2)


(2)


Fire

(2)

Reprodpotent

(2)

Soil

Hydrology

(2)

Biology

(2)

Chemistry

(2)

Structure

(2) Indicators



Case studies: NSW Open Grassy Woodland


How are longitudinal site histories compiled and transformation data derived for each site?


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Compiling and translating historical observations requires three core elements

Where

When What


1800

1850

1900

1750

1950

2000

2050

DNA matching Source ID: 1a

Source ID: 1b

Sequencing historic & contemporary LU & LMP and responses of native vegetation

Year

Final synthesised sequenceMultiple sources


Step 2A: Score impacts of LU & LMP on attributes of regenerative capacity

Step 2B: Score impacts of LU & LMP on attributes of vegetation structure

Step 2C: Score impacts of LU & LMP on attributes of vegetation composition

Step 2: Score impacts relative to a reference condition for each site and year

Step 3: Calculate total scores of impacts of LU/LMP on themes for each site for each year

Step 4 – Graph total scores to illustrate transformation

Step 5– Model spatial and temporal extents of condition at a landscape level, using GIS, remote sensing , ecological models

Step 6 – Validate the results of the spatial and temporal models using independent datasets and peer review

Workflow for deriving impacts of LU and LMP vegetation

Step 1C: Standardise site-based observation using national guidelines for LU & LMP. Fill gaps from regional records

Step 1: Compile primary data on LU and LMP histories for case study sites

Step 1A: Compile and translate and check. Include major natural events e.g. droughts, floods, fires, cyclones

Step 1B: Compile and check data on impacts of LU & LMP on native veg.


Step 1A & 1B: Compile and check disturbance histories

Year LU LMP

1788 Indigenous land management Fire management



1830 Grazing native vegetation Shepherding sheep and cattle

1850 Grazing native vegetation Fences established (set stocking commenced) - freehold land

1860 Grazing of native vegetation Closer settlement – set stocking smaller paddocks

1898 Grazing of native vegetation Prolonged drought set stocking

1900 Grazing native vegetation Woodland areas on higher ground were partly cleared - set stocking

1939 Grazing native vegetation Set stocking sheep


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Land use

codes

Land Management Practices codes PLANTS_VEG (codes 1.n.n.n.n.) on

ANIMALS (codes 2.n.n.n.n) on

SOIL (codes 3.n.n.n.n) on

WATER (codes 4.n.n.n.n)

YearALUM

classification

First

LUMIS

code

Second

LUMIS

code

Third

LUMIS

code

Forth

LUMIS

code

Fifth

LUMIS

code

1788 1.2.5

1815 2.1.0 1.3.1.4

1820 2.1.0 1.3.1.4

Step 1C: Standardise LU & LMPs using national guidelines


Regenerative capacity

Year

RC_fire_burnt_are

a

RC_fire_starts

RC_soil_hyd_surf_w

ater

RC_soil_hyd_gnd_wat

er

RC_soil_phys_dpth_

a

RC_soil_phys_struct

RC_soil_chem_fert_n

pk

RC_soil_chem_trace_elem

RC_soil_biol_invert_recyc

RC_soil_biol_organ_

matt

RC_reprod_potent_

OS

RC_reprod_potent_

US

1788 1 1 1 1 1 1 1 1 1 1 1 11815 1 1 1 1 1 1 1 1 1 1 1 11820 0.9 0.5 1 1 1 1 1 1 1 1 1 11830 0 0 0.9 1 1 1 1 1 1 1 1 11850 0 0 0.7 1 0.8 1 1 1 0.8 0.8 1 11860 0 0 0.5 1 0.7 0.8 0.9 1 0.5 0.6 1 11898 0 0 0.4 1 0.7 0.7 0.8 1 0.3 0.2 1 11900 0 0 0.3 1 0.7 0.6 0.8 1 0.3 0.6 1 11939 0 0 0.3 1 0.7 0.6 0.8 1 0.3 0.6 1 1

Step 2A: Derive scores for regen capacity


Step 2B: Derive scores for vegetation structure

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Vegetation structure

Year

VS_OS_height

VS_OS_fpc

VS_OS_div_age_clas

s

VS_US_height

VS_US_gnd_cov

VS_US_div_age_cla

ss

1788 1 1 1 1 1 11815 1 1 1 1 1 11820 1 1 1 1 1 11830 1 1 1 1 1 11850 0.8 0.6 0.7 0.7 0.7 0.81860 0.6 0.6 0.7 0.5 0.6 0.61898 0.6 0.6 0.7 0.1 0.2 0.31900 0.5 0.3 0.5 0.5 0.4 0.31939 0.5 0.3 0.5 0.5 0.6 0.3


Species composition

Year

SC_OS_fnl_groups

SC_OS_richness

SC_US_fnl_groups

SC_US_richness

1788 1 1 1 11815 1 1 1 11820 1 1 1 11830 1 1 1 11850 0.7 1 0.7 0.91860 0.6 1 0.6 0.71898 0.4 1 0.4 0.61900 0.4 1 0.4 0.61939 0.4 1 0.4 0.6

Step 2C: Derive scores for species composition


Step 3: Calculate total scores of impacts of LU/LMP for each site & year (benchmarked)

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Year Regenerative capacity (%)

Vegetation structure (%)

Species composition (%)

1788 55 27 18

1815 55 27 18

1820 52 27 18

1830 45 27 18

1850 41 20 15

1860 36 16 13

1898 32 11 11

1900 33 11 11

1939 33 12 11


Step 4: – Graph total scores to illustrate vegetation transformation


Step 4: – Graph scores for diagnostic attributes

Benchmark scores


How are this site-based scores validated?


Certainty level standards for the LUMIS historical records

Geocode accuracy

Temporal accuracy

LU or LMP accuracy

Spatial extent

accuracy

HIGH "Definite"

MEDIUM "Probable"

LOW "Possible"


Year List of LU and LMP history NSW_Talaheni_Murrumbateman: 34,58,1.94S;149,10,41.15E

1788 Indigenous land management1825 First explorers in the district 1830 Grazing of native vegetation (shepherds)1850 Fencing and set stocking with sheep commenced 1860 Pre-clearing of overstorey set stocking with sheep continues1900 Overstorey cleared1962 Overstorey thinned to promote grazing 1980 Commenced rehabilitation toward native vegetation1983 Area grazed using pulse grazing in drought1986 Area continues to be used for pulse grazing in drought

1997 Manage the stand composition and structure to meet multiple outcomes

2004 Continuing to light graze with sheep in droughts


Belconnen Naval Transmitter Station rainfall anomaly 1900-2010

Drought years

YearsSource: BOM

Rai

nfal

l ano

mal

y

‘Good years’

11 year trend line


Assumptions of this approach

• Changes in LU & LMP

– result in predictable changes in structure, floristics & regen capacity

– can be consistently and reliably differentiated from natural events

– are adequately and reliably documented over time

– can be reliably used to score changes in vegetation transformation

• Sequential changes in veg transformation over time can be

represented at sites and landscapes

48


49

Types of data and information

1. Mainly - text-based e.g. • Land use and land management history

• Environmental history

• Ecological history

• Other

2. Mainly spatial - maps and models incl. remotely images/GIS– ecological sources

– land use and LMP sources

– Geographical and historical sources

– other

Older & more qualitative

More recent & more quantitative


1750 1800 1850 1900 1950 2000 2050

0

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2

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X, Y Tas Midlands

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X, Y Tas Midlands

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How might this information be used to address the big issues?

Hypothetical


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How might this information be used to address the big issues?

• Opportunities• Options• Tradeoffs

Hypothetical


Provides a basis for a conversation with a land manager

Benchmark scores

?

??


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score

Att

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sProvides a basis for a conversation with a land manager


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Fire

(2)

Reprodpotent

(2)

Soil

Hydrology

(2)

Biology

(2)

Chemistry

(2)

Structure

(2) Indicators



1

3

10

22

Dia

gn

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score

Att

rib

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gro

up

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VegetationStructure

Overstorey

(3)

Understorey

(3)


Fire

(2)

Reprodpotent

(2)

Soil

Hydrology

(2)

Biology

(2)

Chemistry

(2)

Structure

(2) Indicators



1

3

10

22

Dia

gn

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trib

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score

Att

rib

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gro

up

s

VegetationStructure

Overstorey

(3)

Understorey

(3)

SpeciesComposition

(2)


(2)


Fire

(2)

Reprodpotent

(2)

Soil

Hydrology

(2)

Biology

(2)

Chemistry

(2)

Structure

(2) Indicators



Step 5 – Scaling up to landscape levels

Static layers• first contact by European explorers• slope & relief derived from 30m DEM• aspect classes derived from 30m DEM• weathering layer• digital atlas of soils+• pre-European vegetation types (NVIS)

Time series response variables• rainfall anomaly (post 1900)• state-wide & national land tenure• FPC (post 1980s)*• ground cover (post 1980s)*• NDVI / EVI (post 1980s)*• native veg (tree) layers*• state-wide & national land use

• sheep DSE• cattle DSE• cropping• urban areas• Plantations• nature conservation reserves• indigenous protected areas

• Infrastructure• railways• roads

• fire regime (fire area & No. fire starts)*• otherTERN AusCover*

TERN Soils+


Landform Pattern and Topographic Position Index. 30 m – DEM SRTM

Nass Valley - ACT


61

Transformed native vegetation informing future land use options

1788 1800 1850 1900 1950 2000

2010

2050 Scen 1 2050 Scen 2 2050 Scen 3 2050 Scen 4

Before 2010

Current

Future scenarios – the big issues


Vision for the future

• Recognition of the benefits of compiling site-based contemporary LU & LMP

• Greater awareness of consequences LU & LMP and the responses of native vegetation i.e. +ve and –ve

• Discoverable and accessible data and info via a national repository

? when and where landscapes were and are being transformed


• Process is tedious

• Preliminary site-based results are promising

• Independent datasets & peer review needed to validate results

Conclusions


Acknowledgements

• TERN ACEAS for funding my sabbatical fellowship at UQ in Brisbane

• CSIRO Ecosystems Sciences, Canberra for hosting me in Canberra

• ABARE-BRS, Greening Australia, Forestry NSW, CSIRO ES, John Ive and others for providing datasets


Thank you

thackway_may_presentation

Technology

native vegetation insights

case study